3 papers
cs.IR2026
Bridging Semantic Understanding and Popularity Bias with LLMs
Renqiang Luo, Dong Zhang, Yupeng Gao +5
Semantic understanding of popularity bias is a crucial yet underexplored challenge in recommender systems, where popular items are often favored at the expense of niche content. Mo…
cs.CL2026
When to Invoke: Refining LLM Fairness with Toxicity Assessment
Jing Ren, Bowen Li, Ziqi Xu +6
Large Language Models (LLMs) are increasingly used for toxicity assessment in online moderation systems, where fairness across demographic groups is essential for equitable treatme…
cs.LG2024
Federated Knowledge Transfer Fine-tuning Large Server Model with Resource-Constrained IoT Clients
Shaoyuan Chen, Linlin You, Rui Liu +2
The training of large models, involving fine-tuning, faces the scarcity of high-quality data. Compared to the solutions based on centralized data centers, updating large models in…